Devices, systems and methods for digital image analysis

ABSTRACT

The disclosed devices, systems and methods relate to various devices, systems and methods related to objectively analyzing digital images of turfgrass to rate various parameters and to objectively measure overall quality. The system establishes thresholds and may execute a series of steps to determine green coverage, color, density, and uniformity. The system can scale images to determine uniformity.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/325,826 filed 23 Oct. 2017 and entitled “Devices, System and Methodsfor Digital Image Analysis” which is hereby incorporated by reference inits entirety under 35 U.S.C. § 119(e).

TECHNICAL FIELD

The disclosed technology relates generally to a digital analysisplatform, and in particular, to devices, systems and methods forprocessing digital images of turfgrass.

BACKGROUND

The disclosure relates to devices, systems and methods for analyzingdigital images of turfgrass. In particular analyzing digital images toquantify various parameters of turfgrass and objectively evaluateoverall turfgrass quality.

The ability to objectively quantify turfgrass parameters and overallquality is important for turfgrass scientists. Turfgrass quality can bedetermined by the combined effects of coverage, color, density, anduniformity.

Digital photography has become a common and affordable means for thescientific community to document and present images. Through digitalphotographs, researchers can instantaneously obtain millions of bits ofinformation on variously sized plots of turfgrass from very small tovery large. Each pixel in an image contains color information about thearea captured by that pixel.

Currently turfgrass quality is determined subjectively by turfgrassscientists. Alternatively, SigmaScan™ software can be used to determinecoverage and color. Subjective determinations of turfgrass quality areundesirable because there is no standardization and each individual mayrate quality parameters differently creating difficulty in comparingquality across time and different areas. SigmaScan™ has manydisadvantages including that it only quantifies ground coverage andcolor and is slow at processing images. Further, the existing softwareis inadequate because it leaves out parameters for turfgrass qualityanalysis and is not able to calculate overall quality.

There is a need in the art for a system to objectively determinecoverage, color, density, and uniformity of turfgrass from a digitalimage. It is further desirable to provide a system to objectivelymeasure and quantify overall turfgrass quality from various parameters.It is further desirable to provide a system for determining turfgrassparameters and overall quality quickly.

BRIEF SUMMARY

This disclosure relates to devices, systems and methods for objectivelyanalyzing turfgrass through digital images, specifically by objectivelymeasuring various turfgrass parameters and creating an objectiveanalysis of overall turfgrass quality. Described herein are variousembodiments relating to devices, systems, and methods for improvingturfgrass analysis.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Theturfgrass analyzing system disclosed herein is capable of determiningvarious parameters from a digital image including green coverage, color,density, and uniformity, determining overall turfgrass quality,utilizing a frame within an image to define the area to be analyzed, andperforming analysis of a large plot.

Some embodiments include corresponding computer systems, apparatus, andcomputer programs recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

Implementations may include one or more of the following features. Aturfgrass analyzing system comprising a storage device for storage ofdigital images and a processor for analyzing digital images wherein theprocessor is configured and arranged to analyze a defined set ofparameters. The storage device containing an image of turfgrass. Thesystem wherein threshold values can be set to remove pixels from theimage of turfgrass. The system wherein the image contains a frame ofcontrasting color. The system wherein the defined set of parametersincludes green coverage, color, density, and uniformity. The systemwherein overall turfgrass quality is determined from a weighted averageof the defined set of parameters.

Some implementations may include one or more of the following features.A method comprising obtaining a digital image of turfgrass, receiving bya storage device the digital image, retrieving by a processor thedigital image from the storage device, and processing by the processorthe digital image to determine a defined set of parameters. The methodwherein the defined set of parameters includes green coverage, color,density, and uniformity. The method further comprising determiningoverall quality from the defined set of parameters. The method whereingreen coverage is determined by setting a set of threshold values,removing pixels outside of the set of threshold values, determining thenumber of green pixels relative to the total number. The method whereincolor is determined by calculating the average DGCI value for the image.The method wherein density is determined by determining the number ofshadows in the digital image. The method wherein uniformity isdetermined by scaling the digital image, grouping areas of similar colorin the scaled image, and comparing the size of the areas of similarcolor to the digital image.

One general aspect includes a computing device comprising a storagedevice, a processor, and a display wherein the processor retrieves adigital image from the storage device, the processor is configured tocalculate turfgrass quality from the digital image, and the processordisplays on the display the digital image and turfgrass quality. Thedevice wherein the digital image contains a frame of a color in contrastto green. The device wherein turfgrass quality is determined by aweighted average of measurements of green coverage, color, density, anduniformity. The device wherein green coverage is determined by setting aset of threshold values, removing pixels outside of the set of thresholdvalues, determining the number of green pixels relative to the totalnumber. The device wherein color is determined by calculating theaverage DCGI value for the image. The device wherein density isdetermined by determining the number of shadows in the digital image.The device wherein uniformity is determined by scaling the digitalimage, grouping areas of similar color in the scaled image, andcomparing the size of the areas of similar color to the digital image.

One or more computing devices may be adapted to provide desiredfunctionality by accessing software instructions rendered in acomputer-readable form. When software or applications are used, anysuitable programming, scripting, or other type of language orcombinations of languages may be used to implement the teachingcontained herein. However, software need not be used exclusively, or atall. For example, some embodiments of the devices, methods and systemsset forth herein may also be implemented by hard-wired logic or othercircuitry, including but not limited to application-specific circuits.Firmware may also be used. Combinations of computer-executed software,firmware and hard-wired logic or other circuitry may be suitable aswell.

While multiple embodiments are disclosed, still other embodiments of thedisclosure will become apparent to those skilled in the art from thefollowing detailed description, which shows and describes illustrativeembodiments of the disclosed apparatus, systems and methods. As will berealized, the disclosed apparatus, systems and methods are capable ofmodifications in various obvious aspects, all without departing from thespirit and scope of the disclosure. Accordingly, the drawings anddetailed description are to be regarded as illustrative in nature andnot restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic diagram of the system, according to oneimplementation.

FIG. 1B is a schematic diagram of the system, according to oneimplementation.

FIG. 2A is a depiction of the display showing an original image,according to one embodiment.

FIG. 2B is a depiction of the display showing the original image of FIG.2A with threshold settings applied, according to one embodiment.

FIG. 3 is an exemplary embodiment of the system selecting thresholdvalues, according to one embodiment.

FIG. 4 is a graph depicting density ratings.

FIG. 5 is a depiction of uniformity analysis, according to oneembodiment.

FIG. 6 is a graph depicting uniformity ratings.

FIG. 7 is an exemplary embodiment of the display for setting weightswhen determining overall quality.

FIG. 8A is an exemplary original image of turfgrass within a frame.

FIG. 8B is an depiction of FIG. 9A after applying frame values.

FIG. 9 is an exemplary implementation of the display setting framevalues for frame analysis.

FIG. 10 contains various exemplary photographs of turfgrass.

DETAILED DESCRIPTION

The various embodiments disclosed or contemplated herein relate toimproved devices, systems and methods for analyzing digital images,specifically of turfgrass. Some earlier processes for digitallyanalyzing turfgrass are described by Karcher, D. E., and M. D.Richardson. 2003. Quantifying Turfgrass Color Using Digital ImageAnalysis. Crop Sci. 43:943-951. doi:10.2135/cropsci2003.9430 andRichardson, M. D., D. E. Karcher, and L. C. Purcell. 2001. QuantifyingTurfgrass Cover Using Digital Image Analysis. Crop Sci. 41:1884-1888.doi:10.2135/cropsci2001.1884, which are hereby incorporated by referencefor all purposes.

The disclosed devices, systems and methods relate to a system capable ofobjectively analyzing digital images of turfgrass to rate variousparameters and overall quality. The devices, systems and methodsdiscussed herein are merely illustrative and are not to be interpretedas limiting in scope. While the various devices, systems and methods aredescribed herein as a “system” this reference is made for brevity,rather than to limit the scope of any particular embodiment.

The various implementations of the disclosed system, devices and methodsare constructed and arranged to process digital images for turfgrassquality parameters, including green coverage, color, density, uniformityand the like. Other parameters are of course possible. The systemcontains a Java application in certain implementations, but can alsoinclude various other types of applications or platforms, as would beknown to those of skill in the art. In certain implementations, theprogram optionally runs on Windows, Mac, and Linux operating systems,but could be used in conjunction with other operating systems as wouldbe known.

In various implementations, the system allows for the objectivequantification of turfgrass quality via digital image analysis. Thesystem gives a measure of turfgrass quality of an image by performingeach of the following analyses according to certain implementations,linearly interpolating the results to a user-specified scale, andcalculating a weighted average of the scaled results. This weightedaverage is a measure of quality according to these implementations.Further details and description are found below. In variousimplementations, a series of steps are performed, which can be executedin any order.

Turning to the drawings in greater detail, exemplary implementation ofthe system 10 are shown in FIGS. 1A and 1B. In one implementation, inone step a digital image 12 of turfgrass is taken (box 102), creating adigital image. In various implementations, the digital image 12 can betaken by a digital camera 14 or other photography device as would beunderstood by those of skill in the art.

In another step, the digital image 12 is stored (box 104) in a storagedevice 16, such as an in-camera memory card, cloud-based storage, orother storage device as would be known in the art.

In another step, a processor 18 retrieves (box 106) the desired image orimages from the storage device 16.

In another step, the processor 18 processes (box 108) the digital imageon a pixel-by-pixel level to obtain various parameters from the digitalimage 12 such as coverage (box 112), color (box 114) and density (116).

In certain implementations, in an additional step, the processor 18 mayfurther determine overall turfgrass quality (box 120) via comparison toa standard.

In yet further implementations, in an additional step, the image 12 isscaled—as discussed below in relation to FIG. 5—and uniformity isestablished via the scaled images (box 118).

In a further optional step, the system 10 may additionally include adisplay 20 to display (box 110) the digital image 12, parameters,overall quality to a user, and/or other values to a user.

It is understood that the information contained in each digital imageincludes the amount of red, green, and blue light (“RGB”) light emittedfor each pixel in the digital image. To ease the interpretation ofdigital color data, RGB values can be converted directly to hue,saturation and brightness (“HSB”) values that are based on humanperception of color. For example, in HSB color descriptions hue isdefined as an angle on a continuous circular scale from 0° to 360°(0°=red, 60°=yellow, 120°=green, 180°=cyan, 240°=blue, 300°=magenta),saturation is the purity of the color from 0% (gray) to 100% (fullysaturated color), brightness is the relative lightness or darkness ofthe color from 0% (black) to 100% (white).

Returning to the implementation of FIG. 1A, the processor 18 candetermine green coverage of turfgrass from a digital image 12 (also showin FIG. 1B boxes 108 and 112). To analyze green coverage, the processordetermines, the hue, saturation, and brightness (“HSB”) for each pixelof the digital image 12 from the RGB values using a standard processknown to those of skill in the art.

For the processor 18 to analyze the image for green coverage thresholdsettings must be set. Threshold settings include HSB ranges. A user mayselect various ranges of HSB such that any pixel that has an HSB valuewithin the selected range will be included in the processing. Any pixeloutside of the selected HSB ranges will not be included in theprocessing. As such, ranges should be selected to include only thosepixels that the user wants to evaluate. For example, a user may selectthreshold ranges such that only those pixels that contain greenturfgrass will be included in the analysis and exclude pixels capturingsoil or other debris.

The system 10 may include default threshold settings, for example hue70°-170°, saturation 10%-100% and brightness 0%-100%. If no defaultsettings are provided or customization is desired the system 10 mayallow for selecting various HSB ranges as desired. The thresholdsettings may be adjusted for a variety of reasons including to correctfor camera or lighting effects.

As shown in FIG. 2B, if the threshold settings are unknown, the HSBranges can be varied and the system 10 may be configured to turn anypixel not within the ranges black. FIG. 2A, shows the digital image inits original state. FIG. 2B shows the digital image with the thresholdsettings applied such that the pixels in the image that are outside theselected ranges have been blacked out. Once the appropriate thresholdlevels are determined the analysis can be completed. As described above,pixels that are outside of the threshold ranges will not be included inthe analysis. The process of blacking out the pixels outside of thethreshold ranges and displaying the image with the blacked out pixelsallows for quick and easy fine tuning of the threshold ranges in orderto obtain a more accurate analysis.

In other implementations, such as the implementation shown in FIG. 3,the system 10 can display the original digital image 12 simultaneouslywith the image with threshold settings applied and appropriate pixelsblacked out. The system 10 can be configured in various ways to allowfor the appropriate threshold values to be selected. In someimplementations, the system 10, allows for a user to zoom in on adigital image 12 to better fine tune the threshold settings. The zoomedin image 13 may be displayed next to the original digital image 12.

In various implementations of the system 10, green coverage isdetermined by the number of pixels within the image that are within theselected HSB values for green turfgrass compared to the total number ofpixels. An analysis of coverage can be used to quantify seedling orspring establishment, drought resistance, pest resistance, and/or springgreen-up. Green coverage also provides a part of the overall qualityanalysis.

The system 10 can objectively analyze the color of the turfgrass bydetermining the average color of the image (shown in FIG. 1B box 114).The average color of the image may be determined using the dark greencolor index (“DGCI”). For example, high DGCI values generally correspondto healthy turf with relatively high chlorophyll content as well as turfcultivars with dark genetic color. In some embodiments, the system 10can perform color analysis for the entire image. In other embodiments,the system 10 can use the threshold ranges as described above tocalculate average color using only the desired pixels within the image.For example, a user may select threshold values such that the analysiswill only capture those pixels representing green turfgrass whileexcluding pixels capturing dirt, soil, or other non-turfgrass particles.

The system 10 can quantify the density of turfgrass from a digital image12 (shown in FIG. 1B box 116). Density is a measure of the number ofplants per unit area. The system 10 determines density using shadows inthe digital image 12. A shadow is defined as a group of bordering darkpixels. Dark pixels are defined by a HSB threshold, that can be selectedand varied by a user or preset within the system 10. A high number ofshadows indicates a high density, while a low number of shadowsindicates a low density. Specifically, as would be appreciated by theskilled artisan, more dense turfgrass would have many non-contiguousshadows, whereas less dense turfgrass will have fewer but largershadows. Turf density is an indicator of overall turf health andaesthetic quality. Density analysis can be used to quantify geneticimprovements in turf cultivars.

Exemplary threshold ranges for selecting shadows or dark pixels are hue:0-360°, saturation: 0-100%, and brightness: about 0-about 23%. Otherranges may be selected as necessary for the digital image to beanalyzed. For optimal image analysis, all images should be taken understandardized conditions, such as the same height, lighting, and camerasettings.

FIG. 4 shows a linear regression of the number of shadows found in adigital image 12 and a corresponding visual density rating on a scalefrom 1-9. As shown in FIG. 4, there is a high correlation between thenumber of shadows as determined by the system 10 and a visual rating ofdensity. A lower number of shadows yields a lower rating, while a highernumber of shadows yields a higher density rating.

In one step, the system 10 determines uniformity from the digital image12 (FIG. 1B box 118). The system 10 processes the digital image 12 onthe processor 18 to determine the percentage of the digital image 12having the same or similar color and thereby determine uniformity. Theuniformity of turfgrass corresponds to how the turfgrass appears from adistance. A low resolution digital image can give an approximation ofhow turfgrass would appear from a distance (seen in FIGS. 5D and 5F).

In one step, the processor 18 retrieves a digital image 12 from thestorage device 16. The processor 18 scales the digital image 12 (FIG. 5Cand in 5E), for example to a thumbnail size. In some implementations,the image 12 is scaled such that there are approximately 12.62 pixelsper foot, while other ratios are contemplated. The scaling of the imagemay be done iteratively. Iterative scaling may be used to create ascaled image that more accurately represents the original, such as tocreate a smooth final image without noise.

In some implementations, iterative scaling may consist of a series ofsteps. In one step, a processor 18 calculates the ideal dimensions for ascaled image. In another step, the digital image 12 is scaled by using ascale factor to reduce the pixel height and width by the selected scalefactor. The step of scaling the image 12 using a scale factor isrepeated until the image reaches the desired pixel height and width.

In another step, the system 10 may blur the image. The processor 18 mayslightly darken any extremely bright or white pixels.

The processor 18 then partitions the image into contiguous regions ofsimilar color. In some implementations, contiguous regions of similarcolor are determined using a label buffer of the same size and dimensionas the scaled image. Similar color may be defined using the deltaE2000color distance metric, or other metric known to those of skill in theart. In some implementations, two pixels will be considered to have asimilar color if their deltaE2000 color distance is less than about 1.4,while other values may also be used.

For each contiguous region of similar color the average color iscalculated.

In another step the processor 18 groups the contiguous regions ofsimilar color together based on similarity of their average color.Similarity of average color may be determined using the deltaE2000 colordistance metric. For example, groups could be considered to have similaraverage color if their deltaE2000 color distance is less than about 19,while other values are contemplated.

In another step the processor 18 may determine the largest grouping ofcontiguous regions of similar color. In another step, a percentagecorresponding to uniformity is determined by taking the number of pixelsin the largest grouping of contiguous regions of similar color anddividing by the number of pixels of the whole scaled image.

Uniformity estimates the consistency of a turf canopy's appearance whenviewed from standing above the surface. Turf uniformity is a measure ofoverall plant health and cultivar purity within the canopy. Uniformityalso plays a role in aesthetic turf quality.

A low percentage of uniformity corresponds to the largest region ofsimilar color being small relative to the entire image and may result ina low uniformity rating. A high percentage of uniformity corresponds tothe largest region of similar color being large relative to the entireimage and may result in a high uniformity rating. A high uniformityrating may correspond to high overall plant health and cultivar purity.

FIG. 6 shows a linear regression of the uniformity percentage ascalculated by the system 10 and visual uniformity ratings on a scale of1 to 9. The system 10 determination of uniformity has a high correlationwith visual uniformity ratings.

The system 10 can be used to quantify overall turfgrass quality (FIG. 1Bbox 120). Using combined analyses of color, coverage, density and/oruniformity overall turfgrass as described herein, overall quality can bedetermined. In one step, the values determined for color, coverage,density and/or uniformity are converted to rating values, such as bylinear interpolation (see FIG. 7). Rating values may be on a scale from1 to 9, but other scales may be used. The rating scale of 1 to 9 is wellunderstood in the art. A weighting scale may be selected by a user tocorrespond to the parameters considered most, least, and/or equallyimportant to overall quality by that user. A weighted average of thosevalues can then be determined according to a weighting scale selected bythe user.

FIG. 7 shows an exemplary display 20 for inputting weighting scalevalues. A user inputs a maximum and minimum value rating for eachparameter to be considered in the overall quality analysis. For exampleeach parameter (coverage, color, density, and uniformity) can be set ona scale from 1 to 9, or any other numerical scale as desired. The usercan select the weight to be given to each parameter. For example if eachparameter has equal weight a value of 1 can be entered for allparameters. In another example, if coverage has twice the importance ofcolor, density, and uniformity in determining overall quality a value of2 should be entered for coverage, while a value of 1 should be enteredfor all other parameters.

The processor 18 using the values inputted by the user can calculateoverall quality. The system 10 may be configured to generate a read outof the intermediate parameter values, the corresponding parameterratings, and the overall quality rating for each digital image 12processed.

As shown in FIGS. 8A-B and 9, the system 10 may analyze turfgrass withinframe or a portion of a digital image. Frame analysis can be useful foranalyzing small areas or non-rectangular areas. Examples of small ornon-rectangular areas include but are not limited to greenhouse pots,ball marks, divots and lysimeters.

As seen in FIG. 8A, to conduct this analysis a frame 22 is includedaround the turfgrass area of interest within the digital image 12. Thedigital image 12 should include only the turfgrass area to be analyzedand the frame 22. The frame 22 must be of a color that contrasts withthe color of the turf and/or soil within the digital image 12. Examplesof colors contrasting with turf and soil include but are not limited topink and purple. The frame 22 may be constructed of various materialsincluding poster board or cardstock, while other materials could beused.

FIG. 9 shows an exemplary implementation of the display 20 for settingframe threshold values. The system 10 allows for selecting the HSBvalues for the frame 22 such that the frame 22 will be excluded from theanalysis, shown in FIG. 8B. If the HSB values for the frame 22 areunknown the frame threshold values can be determined by varying theparameters until the pixels of the frame turn black, similar to theprocess for selecting threshold ranges for green turfgrass describedabove. Additionally, the frame analysis allows for selecting thresholdvalues such that only pixels representing turfgrass within the frame 22are included in the analysis, see FIG. 8B. The non-turfgrass pixelswithin the frame 22 may also be excluded from the analysis.

In certain implementations a machine learning model is used to identifycharacteristics of turfgrass and establish parameters, ratings andthresholds, and can be used to revise the other systems, methods anddevices described herein, such as by refining the ratings, thresholdsand standards (described in relation to FIGS. 2, 3, 5, and 7-9) toimprove accuracy of the system 10. In these implementations, a model isused to associate digital image data within a computing machine, such asa server 17 or database 17.

Generally, the various machine learning approaches, may be coded forexecution on the processor 18, server 17, a database 17, third partyserver or other computing or electronic storage device in operablecommunication with the processor 18.

The model may be executed on data recorded or otherwise gathered fromdigital images 12. In various implementations, the data may include, butis not limited to, one or more of the following: expert rating forparameters such as coverage, color, density, and uniformity; and outputfrom the system under various sets of inputs such as HSB thresholds todetermine pixels corresponding to green turfgrass.

Accordingly, the system 10 and methods using the machine learning modelmay send and/or receive information from various computing devices, aswell as a database or other collection of representative turfgrassimages across various cultivars, taken under controlled lightingconditions by way of a gateway or other connection mechanism. In certainembodiments, the systems and methods may utilize image data incombination with expert ratings and corresponding inputs to improveaccuracy of the evaluation performed in conjunction with the system 10,and associated devices and methods.

In various implementations, image data may also be loaded onto any ofthe computer storage devices of a computer to generate an appropriatetree algorithm or logistic regression formula. Once generated, the treealgorithm, which may take the form of a large set of if-then conditions,may then be coded using any general computing language forimplementation. For example, the if-then conditions can be captured andcompiled to produce a machine-executable model, which when run, acceptsnew image data and outputs results which can include adjusted maximumand minimum standards for various parameters. In variousimplementations, these results can be re-introduced into the learningmodel to continually improve the functions of the system 10, includingupdating the various maximum and minimum standards and thresholds usedthroughout. It is understood that these implementations are also able totrend the respective data values and readings to improve the performanceof the system 10, devices and methods.

For the analyses, multithreading may be implemented to extend theapplication of the system 10 and decrease execution time. The system 10operates at least two orders of magnitude faster than prior systems suchas SigmaScan®. Said another way the system 10 may process images in1/100th of the time of prior systems while performing more analysesincluding coverage, color, density, uniformity, and overall quality. Thesystem 10 works faster by leveraging multicore technology to analyzemultiple images at once, decreasing processing time.

The system 10 may be configured such the analyses of coverage, color,and density can be processed at the simultaneous requiring only one scanof the pixels of the digital image 12. Prior systems require multiplescans of the pixels of an image to receive readings on more than oneparameter.

The system 10 is a technical improvement over prior systems byprocessing analyses of density and uniformity along with coverage andcolor. The system 10 additionally can process an aggregate measure ofquality, described above that was not possible prior. Also, the system10 processes images in less time.

Examples

FIG. 10 contains multiple images A-D of turfgrass that can be processedusing the above described analyses. The coverage, color, density, anduniformity for these digital images of FIG. 10A-D can be processed onthe processor 18. One step is to select the appropriate threshold valuesas described above, in this example hue 55-140, saturation 10-100, andbrightness 0-100 were used.

To measure overall quality a user may enter the maximum and minimumrating values as desired. In this example, rating ranges were set tocover 3-9, color 4-8, density 4-8, and uniformity 2-8. The weight to begiven to each selected variable may also be chosen. In this example,weights were set to cover 4, color 1, density 2, and uniformity 3.

A coverage analysis was performed using the above described system 10and process. Turning to FIG. 10, a percent cover and a cover qualityrating were processed for each image A-D. FIG. 10A, percent cover 98.35and cover quality rating 8.68. FIG. 10B, percent cover 95.86 and coverquality rating 8.16. FIG. 11C, percent cover 71.41 and cover qualityrating 3.00. FIG. 10D, percent cover 99.85 and cover quality rating9.00.

A color analysis was performed on each of the digital images of FIG. 10corresponding to the above described system 10. FIG. 10A color rating 7.FIG. 10B color rating 4.49. FIG. 10C color rating 4.00. FIG. 10D colorrating 8.00.

A density analysis was performed on each of the digital images of FIG.10, corresponding to the above described system 10. FIG. 10A densityrating 8.00. FIG. 10B density rating 4.49. FIG. 10C density rating 4.00,FIG. 10D density rating 4.43.

A uniformity analysis was performed for each of the digital images ofFIG. 10 corresponding to the above described system 10. FIG. 10Auniformity rating 8.00. FIG. 10B uniformity rating 5.74. FIG. 10Cuniformity rating 3.36. FIG. 10D uniformity rating 2.00.

The processor 18 may use the parameters of coverage, color, density, anduniformity as well as weighting values to determine the overall qualityof the turfgrass in each digital image, as described above. FIG. 10Aoverall quality 8.17. FIG. 10B overall quality 6.36. FIG. 10C overallquality 3.41. FIG. 10D overall quality 5.89.

Although the disclosure has been described with reference to preferredembodiments, persons skilled in the art will recognize that changes maybe made in form and detail without departing from the spirit and scopeof the disclosed apparatus, systems and methods.

What is claimed is:
 1. A turfgrass analyzing system comprising: (a) astorage device for storage of digital images, the digital imagescomprising a green coverage parameter, a color parameter, a densityparameter, and a uniformity parameter; and (b) a processor for analyzingdigital images, the processor in communication with the storage device,wherein the processor is constructed and arranged to analyze a definedset of parameters in a digital image, and wherein the defined set ofparameters comprises green coverage, color, density, and uniformity. 2.The system of claim 1, wherein the storage device contains one or moredigital images of turfgrass.
 3. The system of claim 2, wherein thresholdvalues can be set to remove pixels from the one or more digital imagesof turfgrass.
 4. The system of claim 2, wherein each of the one or moredigital images of turfgrass contains a frame.
 5. The system of claim 2,further comprising a database in communication with the processor,wherein the system is constructed and arranged to execute machinelearning on data stored in the database.
 6. The system of claim 5,wherein overall turfgrass quality is determined from a weighted averageof the defined set of parameters.
 7. A method for digital image analysiscomprising: obtaining a digital image of turfgrass via an imagingdevice, the digital image comprising a green coverage, a color, adensity, and a uniformity; receiving by a storage device the digitalimage; retrieving by a processor the digital image from the storagedevice; processing by the processor the digital image by executing oneor more steps to determine a defined set of parameters, wherein thedefined set of parameters includes green coverage, color, density, anduniformity.
 8. The method of claim 7, further comprising scaling thedigital image.
 9. The method of claim 8, further comprising determiningoverall quality from the defined set of parameters.
 10. The method ofclaim 7, wherein green coverage is determined by setting a set ofthreshold values; removing pixels outside of the set of thresholdvalues; and determining the number of green pixels relative to the totalnumber of pixels.
 11. The method of claim 7, wherein color is determinedby calculating the average DGCI value for the image.
 12. The method ofclaim 7, wherein density is determined by calculating the number ofshadows in the digital image.
 13. The method of claim 7, whereinuniformity is determined by: scaling the digital image; grouping areasof similar color in the scaled image; and comparing the areas of similarcolor to the digital image.
 14. A computing device comprising: (a) astorage device; (b) a processor in communication with the storagedevice; and (c) a display in communication with the processor, whereinthe processor retrieves a digital image from the storage device, whereinthe processor is configured to calculate turfgrass quality from thedigital image, and wherein the processor displays the digital image andturfgrass quality on the display.
 15. The device of claim 14, whereinthe digital image contains a frame of a color in contrast to green. 16.The device of claim 14, wherein turfgrass quality is determined by aweighted average of measurements of green coverage, color, density, anduniformity.
 17. The device of claim 16, wherein coverage is determinedby setting a set of threshold values; removing pixels outside of the setof threshold values; and determining the number of green pixels relativeto the total number.
 18. The device of claim 16, wherein color isdetermined by calculating the average DGCI value for the image.
 19. Thedevice of claim 16, wherein density is determined by determining thenumber of shadows in the digital image.
 20. The device of claim 16,wherein uniformity is determined by: scaling the digital image; groupingareas of similar color in the scaled image; and comparing the areas ofsimilar color to the digital image.